Fingerprint based biometric matching continues to be a leading topic of research in the field of image analysis. Tremendous amounts of money and resources have been spent on analyzing fingerprints to match them accurately, robustly, and efficiently. Although great leaps and bounds have been achieved in fingerprint matching technology, there is still room for improvement. Currently, the performance of biometric matching is at an all time high, but operational demands continue to grow. As biometric databases increase in size, there is an equally growing demand for more processing power. One key goal is to increase processing speed without compromising matching efficiency. Conventional techniques address the issue of speed by purchasing faster computers. However, this solution fails to address the issue of efficiency. Efficiency can only be addressed by changing the way in which biometric data is processed.
Current state-of-the-art fingerprint matchers are quite fast and robust as far as 1:1 verification is concerned, but these conventional systems are less efficient at comparing a query sequentially against a large set of gallery fingerprints for identification tasks. In an effort to maximize efficiency, indexing schemes have been proposed. Generally, the theory behind an indexing scheme is to create an index gallery of biometrics using suitable features so that sequential matching is not required for identification. Ideally, after a biometric image is indexed, it does not require any additional post-processing step prior to matching.
Indexing schemes rely on particular characteristics of a biometric image and those characteristics are then used to index the biometric. For example, most existing approaches use minutiae graphs to characterize fingerprint images. Under this technique, the geometry of a minutiae graph of one fingerprint is compared to the geometry of other minutiae graphs stored in a biometric database. Although this technique is faster than comparing two biometric images pixel by pixel, this technique is still time-consuming and results in matching errors. In addition to the large number of required geometric calculations, the computed geometric values are prone to error if the minutiae points are slightly unclear.